A latent factor model for highly multi-relational data - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2012

A latent factor model for highly multi-relational data

Résumé

Many data such as social networks, movie preferences or knowledge bases are multi-relational, in that they describe multiple relations between entities. While there is a large body of work focused on modeling these data, modeling these multiple types of relations jointly remains challenging. Further, existing approaches tend to breakdown when the number of these types grows. In this paper, we propose a method for modeling large multi relational datasets, with possibly thousands of relations. Our model is based on a bilinear structure, which captures various orders of interaction of the data, and also shares sparse latent factors across different relations. We illustrate the performance of our approach on standard tensor-factorization datasets where we attain, or outperform, state-of-the-art results. Finally, a NLP application demonstrates our scalability and the ability of our model to learn efficient and semantically meaningful verb representations.
Fichier principal
Vignette du fichier
serlov_nips12.pdf (156.95 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00776335 , version 1 (15-01-2013)

Identifiants

  • HAL Id : hal-00776335 , version 1

Citer

Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, Guillaume Obozinski. A latent factor model for highly multi-relational data. Advances in Neural Information Processing Systems 25 (NIPS 2012), Dec 2012, Lake Tahoe, Nevada, United States. pp.3176-3184. ⟨hal-00776335⟩
1600 Consultations
1065 Téléchargements

Partager

Gmail Facebook X LinkedIn More